Workplace Cyberbullying Among Healthcare Workers: A Systematic Review of the Prevalence, Antecedents and Consequences
Bibliographic record
Abstract
Workplace cyberbullying is a growing issue that raises serious public health concerns due to its potential for physical and emotional harm. Previous studies on workplace bullying in the healthcare industry have mainly focused on traditional bullying or explored cyberbullying's effect in specific regions or demographic groups. This study aims to systematically review the prevalence, antecedents and consequences of workplace cyberbullying among healthcare workers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines, four academic databases (i.e., Web of Science, PubMed, Scopus and EBSCO) were systematically searched on July 31, 2025. Data were extracted on cyberbullying characteristics, classification, prevalence, antecedents and consequences. Out of 821 studies, 21 were eligible for inclusion. The study's results indicate that victimisation rates of workplace cyberbullying among healthcare workers range from 1.5% to 46.6%, with an incidence rate of workplace cyberincivility of 36.8% for nurses. Drawing on the Social-Ecological Model and Organisational Conflict Theory, the antecedents of workplace cyberbullying among healthcare workers can be classified at the individual, organisational and social levels. Consequences include personal and work-related outcomes. This systematic review suggests that the prevalence of workplace cyberbullying among healthcare workers is highly variable and that uniform standards and tools are needed for its measurement. The identified antecedents and consequences are specific and complex, requiring targeted interventions to prevent and manage cyberbullying in healthcare settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".